In this article, we explore a new mining paradigm, called Indoor Stop-by Patterns (ISP), to discover user stop-by behavior in mall-like indoor environments. The discovery of ISPs enables new marketing collaborations, such as a joint coupon promotion, among stores in indoor spaces (e.g., shopping malls). Moreover, it can also help in eliminating the... (more)

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About TSAS

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a new scholarly journal that publishes high-quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective spanning a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually), such
as: geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.READ MORE

Forthcoming Articles

Spatiotemporal co-occurrences are the appearances of spatial and temporal
overlap relationships among trajectory-based spatiotemporal instances
with region-based geometric representations. Assessing the significance of spatiotemporal
co-occurrences plays an important role in the spatiotemporal frequent pattern
mining applications of moving region objects. Currently, a spatiotemporal
version of the popular Jaccard measure is used for measuring the strength of
spatiotemporal co-occurrences. We will demonstrate the shortcomings of the
Jaccard (J) measure when it is used for assessing the significance of
co-occurrences among spatiotemporal instances with highly different
spatiotemporal evolution characteristics. We will present two extended
novel measures (J+ and J*) that address the
problems linked to the J measure.
Our work includes algorithms for the significance measure calculations, the
proofs and explanations about the key properties of measures, and a detailed
experimental evaluation section. Our experiments include in-depth relevancy and
running time analyses demonstrating the suitability of our proposed measures for
spatiotemporal frequent pattern mining algorithms.

Grids are commonly used for presenting spatial data. However, they have not been previously used for analyzing GPS trajectories. Instead, slower and more complicated algorithms based on individual point-pair comparison have been used. We demonstrate how a grid representation can be used to compute four different route measures: novelty, noteworthiness, similarity and inclusion. The measures may be used in several applications such as identifying taxi fraud, automatically updating GPS navigation software, optimizing traffic and identifying commuting patterns. We compare our proposed route similarity measure, C-SIM, to 8 popular alternatives including Edit Distance on Real sequence (EDR) and Frechet distance. The proposed measure is simple to implement and we give a fast, linear time algorithm for the task. It works well under noise, changes in sampling rate and point shifting. We demonstrate that using the grid, a route similarity ranking can be computed in real-time on the Mopsi2014 route dataset which consists of over 6,000 routes. This ranking is an extension of the most similar route search and contains an ordered list of all similar routes from the database. The real-time search is due to indexing the cell database and comes at the cost of spending 80% more memory space for the index. The methods are implemented inside the Mopsi (http://cs.uef.fi/mopsi) route module.